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A comparison of machine learning algorithms for the surveillance of autism spectrum disorder

OBJECTIVE: The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random forests methods have shown promise in speeding up this process, but they lag behind human clas...

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Pubblicato in:PLoS One
Autori principali: Lee, Scott H., Maenner, Matthew J., Heilig, Charles M.
Natura: Artigo
Lingua:Inglês
Pubblicazione: Public Library of Science 2019
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Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC6760799/
https://ncbi.nlm.nih.gov/pubmed/31553774
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0222907
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